The Long-Term Impacts of an Integrated Care Programme on Hospital Utilisation among Older Adults in the South of England: A Synthetic Control Study

Introduction: Reducing hospital use is often viewed as a possible positive consequence of introducing integrated care (IC). We investigated the impact of an IC programme in North East Hampshire and Farnham (NEHF), in southern England, on hospital utilisation among older adults over a 55 months period. Method: We used a Generalised Synthetic Control design to investigate the effect of implementing IC in NEHF between 2015 and 2020. For a range of hospital use outcomes, we estimated the trajectory that each would have followed in the absence of IC and compared it with the actual trajectory to estimate the potential impact of IC. Results: Three years into the programme, emergency admission rates started reducing in NEHF relative to its synthetic control, particularly those resulting in overnight hospital stays. By year 5 of the study overall emergency admission rates were 9.8% lower (95% confidence interval: –17.2% to –0.6%). We found no sustained difference in rates of emergency department (ED) visits, and average length of hospital stay was significantly higher from year 2. Conclusion: An IC programme in NEHF led to lower than estimated emergency admission rates; however, the interpretation of the impact of IC on admissions is complicated as lower rates did not appear until three years into the programme and the reliability of the synthetic control weakens over a long time horizon. There was no sustained change in ED visit rates.

2. From the remaining pool, the 10 most similar GP practices to each treated practice were selected to form the donor control group for the GSC analysis.
The variables used to assess similarity across regions in step one are listed in Supplementary   Table 1. Since the variables are measured on different scales, they were first standardised using inter-decile range standardisation. Similarity was then measured by computing squared Euclidean distances between each region. Practices located within the most similar regions were selected until a pool of around 1,000 practices was reached.
Before proceeding with step two the following exclusions were applied:  practices that opened or closed during the study period  practices with registered patient population sizes outside the range of registered sizes in the treated group  practices with patterns of key outcome variables indicative of reporting errors  practices without records in both ED and inpatient datasets, so the same set of practices could be used across different outcomes A similar process to that used in step one was then applied to identify the most similar practices to each treated practice. The variables used in step two are listed in Supplementary Table 2. Before assessing similarity, variables were weighted according to how predictive they are of the rate of emergency hospital admissions in the total population in the final year of the pre-intervention period, after adjusting for the other variables in the year prior to that. The weight given to each variable was determined by the absolute value of the corresponding T-test statistic, estimated from a regression of the rate of emergency hospital admissions in the year prior to the start of the intervention on the variables for the preceding year. Similarity was measured by calculating the standardised mean difference (SMD) between all pairs of treated and control practices. SMD values were calculated using annual estimates (of the variables listed in Supplementary Table 2) for each of the two years prior to the start date. After ordering control units by decreasing overall similarity the following steps were applied:  select the set of control practices that are nearest to each of the treated practices  arbitrarily order these, excluding any duplicates  add the control practices that are second nearest to each of the treated practices, excluding any practices already selected  repeat, until all control units have been selected This left a final list of control practices ordered by decreasing similarity with the treated practices. The top 100 'most similar' practices from this list were used in the GSC analysis.

Excluded practices
At the time the IC programme was introduced in NEHF in August 2015 the local population was served by 24 general practices. We excluded from the analysis three practices that closed during the follow-up period and a further two practices with incomplete hospital activity data leaving 19 intervention practices. When a practice triggers its closure patients are informed that they should find an alternative local practice to register with. For the three practices that closed we were able to spot patients transferring to one of the other practices retained in the study. These transfers occurred in 2017 and 2019. The two practices excluded because of missing data were relatively small practices. The combined number of patients registered with these two practices was less than five per cent of all patients registered with a NEHF practice. One of the excluded practices was located in an area with a very low level of deprivation (compared with the national average) and the other in an area where the level of deprivation was close to the national average.

Risk adjustment
Generalised synthetic controls (GSC) allows for the inclusion of time-varying covariates. Two groups of risk adjustment variables were used depending on the outcome. For ED visits and emergency admissions outcomes we adjusted for differences in the characteristics of general practice populations (see Supplementary Table 3). For the average length of stay outcome we adjusted for differences in patients admitted to hospital (see Supplementary   Table 4). Rel. difference (%) -1.6 (-5.2 to 3.4) -0.1 (-4.9 to 5.5) -7.2 (-12.1 to -1.6) -1.4 (-8.4 to 5.9) -11.7 (-18.4 to -2.4)

Supplementary Figure B.2: Average effect (difference between NEHF and estimated counterfactual), 18+
year-old population. Admissions outcomes and ED visits are rates per 10,000 population per month; average length of stay is days. Red = confidence interval does not contain zero, blue = confidence interval contains zero. CACSC = chronic ambulatory care sensitive conditions, UCSC = urgent care sensitive conditions.